VIEIRA, L. L.; VIEIRA, Lucas Lima.
Abstract:
While the easy access, very low cost and rapid dissemination of
news through the Internet lead people to consume a huge amount
of information every day, it also creates a big issue that is the wide
spread of fake news. Although there are tons of works regarding
fake news detection, there are very few ones that investigate its
structure deeply. Therefore, to better understand how fake news is
structured, we propose to consider the subjectivity of news under
the premise that the subjectivity levels of legitimate and fake news
are significantly different. For computing the subjectivity level of
news, we rely on a set of subjectivity lexicons built by Brazilian
linguists. We then tagged fake parts of the news articles and split
them by falsehood category. After that, we calculated the Word
Mover’s Distance (WMD) between these parts and the lexicons
to build the subjectivity feature vectors, in order to perform the
experiments.We believe that our findings contribute to the progress
of studies involving fake news detection.
